Influenza is a common respiratory infection with substantial disease and economic burdens. Due to the threat of another global pandemic, significant resources have been devoted to increase influenza surveillance, laboratory capacity and pandemic preparedness worldwide since 2009. Disease burden estimates are critical for evaluating vaccine benefits, for communicating prevention and control messages, and for developing evidence-based policies for resource allocations. There are several major analytical challenges in estimating influenza disease burden. First influenza symptoms are non-specific and testing is conducted at the discretion of healthcare providers. Severe complications (e.g., pneumonia and cardiovascular events) may occur weeks after infection when influenza viruses are no longer detectable or the patient’s symptoms may not suggest influenza. Second, policy-relevant evaluation of influenza burdens at the national or global scales are often limited by the availability of high-quality surveillance data. A common approach is to create multipliers for extrapolating available burden estimates to other locations or larger populations, while introducing considerable uncertainties. There is a pressing need to develop methods and tools to support burden estimation that will increase accuracy, improve precision, enhance multi-partner collaboration, and quantify uncertainty appropriately. In this 2-year exploratory project, we will examine the use of state-of-the-art approaches from epidemiology and evidence synthesis to influenza burden estimation. In Aim 1, we will develop single-site time-series models for attributing counts of adverse respiratory health outcomes to influenza. Our models will address several commonly encountered analytic challenges, including residual temporal autocorrelation, overdispersion, and unmeasured temporal confounders. By leveraging a unique multi-state emergency department (ED) visits database and three national influenza surveillance systems, these methods will be applied to estimate season-specific influenza-associated ED visits for 102 U.S. during the period 2005 to 2018. We will estimate burdens for specific age groups, sex and influenza types. In Aim 2, we will develop data integration models for combining information across multiple sites and perform predictions to sites without burden estimates. This involves the use of privacy-preserving, distributed algorithms for multi-site analyses that can incorporate individual participant data, improve accuracy, account for reporting bias, and potentially encourage participation. Methods will be applied to (1) estimate annual season- specific influenza-associated ED visits in the U.S. nationally, and (2) estimate global burden of influenza- associated hospitalization as part of an ongoing collaboration with the U.S. Centers for Disease Control and Prevention. Anticipated outcomes from this project include (1) feasibility and performance evaluations of the proposed time-series and data integration models; and (2) substantive findings on influenza-associated morbidity as measured by ED visits and hospitalization for respiratory disease. Moreover, models developed in this project are also widely applicable to other respiratory pathogens.

PROJECT NARRATIVE The proposed project will develop and apply statistical models for estimating seasonal influenza disease burden, which are used for evaluating vaccine benefits, for communicating prevention and control messages, and for developing evidence-based policies for resource allocations.